To acquire new customers, and persuade old customers to purchase more, advertisers commonly conduct solicitations. Solicitations may be in the form of direct mailing, phone calling, and e-mailing potential purchasers. Advertisers usually have at their disposal large databases of individuals to potentially solicit. These databases may have been purchased by the advertisers, or the advertisers may have collected the data on their own. The data for any given individual may be as rudimentary as the person's name, phone number, e-mail address, and mailing address, or may be enriched with demographic information. The demographic information may include the person's gender, income bracket, occupation, as well as other information.
A dilemma faced by the advertisers, however, is which individuals to solicit. Some people are likely to make a purchase regardless of whether they receive a solicitation, whereas others are likely to make a purchase only if they receive a solicitation. Some people may never make a purchase, even if the solicitation offers a steep discount in price. Still others may be offended by receiving a solicitation, and change their minds after having initially decided to make a purchase.
Within the prior art, there are at least two approaches for advertisers to follow to decide who to solicit in an advertising campaign. First, an advertiser may solicit everyone in its database, which is referred to as an untargeted approach. This is costly, however, and where the advertiser is offering a price discount, means that potential revenue is lost when the discount is redeemed by consumers who would have made a purchase anyway. The advertiser loses the cost of the solicitation when soliciting people who will never make a purchase, regardless of whether they receive the solicitation. Furthermore, the advertiser loses the business of those individuals who are offended by receiving the solicitation, and who would have otherwise made a purchase.
Second, the advertiser may solicit only some people in the database, which is referred to as a targeted approach. The question then becomes which individuals to target for solicitation. Advertisers may resort to decision theoretic approaches to answer this question. Decision theoretic approaches utilize statistical and probabilistic models to determine which people to solicit. Decision theoretic approaches can use Bayesian networks, decision trees, and other types of statistical models. However, current such approaches usually focus on one of two goals. First, the approaches may try to maximize consumer response to an advertising campaign. The advertiser, however, is not interested so much in maximizing the response, as it is in selling the most items at the highest price. These two goals may not be consistent with one another. For example, maximizing the response of individuals who would have made a purchase regardless of receiving the solicitation is not the aim of the advertiser.
Second, standard machine learning approaches that are used to construct statistical models from observed data are not well suited for the targeted solicitation problem. Particularly, these approaches are unable to incorporate advertiser profit as the ultimate utility of their learned models, and instead usually focus on predictive accuracy. As an example, the targeted solicitation problem requires a statistical model of the probability that a customer will make a purchase, given known attributes of the customer. The best statistical model to solve this problem, however, is not usually the one that yields the best predictive accuracy.
The prior art is thus limited in the tools it offers advertisers to determine which people to solicit. A strategy of soliciting everyone in the advertisers' databases can be counterproductive, whereas prior art decision theoretic approaches have objectives that are not always aligned with the interests of the advertisers. For these and other reasons, there is a need for the present invention.